During coalbed methane (CBM) development and carbon dioxide (CO2) geological sequestration, gas injection into coal seams significantly alters the behavior of the coal-water-gas three-phase interface; however, the molecular-scale mechanisms governing wettability evolution remain insufficiently understood. In this study, a coal-water-gas interfacial system was constructed using molecular dynamics (MD) simulations, in which a gasreservoir region was introduced to mimic dynamic gas injection toward the coal-water interface. A comprehensive set of descriptors, including contact angle, interaction energy, water density distribution, mean square displacement (MSD), hydrogen bonding, radial distribution function (RDF), and interfacial gas concentration, was employed to characterize interfacial structural evolution. The results demonstrate that gas injection markedly modifies coal-water wettability. Relative to vacuum conditions, the presence of gas increases the coal-water contact angle, with CO2 exhibiting the strongest hydrophobic enhancement effect. Increasing the CO2 molar fraction in flue gas progressively weakens coal-water interaction energy and reduces interfacial water accumulation. Elevated temperature promotes water spreading on the coal surface, whereas increased pressure enhances gas enrichment at the interface and suppresses water adsorption. At the molecular level, CO2 molecules preferentially adsorb onto coal surfaces, forming a gas-enriched interfacial layer that occupies active adsorption sites and inhibits water spreading, thereby restructuring the interfacial microenvironment and regulating wettability. This work elucidates the molecular mechanisms governing gas injection-induced wettability alteration at the coal-water interface, providing fundamental insights for optimizing gas injection strategies for enhanced CBM recovery and CO2 geological sequestration.
Green consumption constitutes a critical pathway to mitigating environmental threats and advancing carbon emission reduction. However, entrenched consumption habits and behavioral inertia often lead consumers to default to conventional products, creating a pronounced attitude-behavior gap in which pro-environmental attitudes fail to translate into actual purchases. To address this issue and uncover the dynamic mechanisms underlying consumer decision-making, this study draws on the Reflective-Impulsive Model (RIM) to develop a process model explaining how environmental concern (EC) influences green product purchasing (GPP). We introduce cognitive load (CL) as a moderator and examine the interventional effect of a default option (DO) as a nudging strategy. Using a 3 × 2 × 2 between-subjects experiment, the findings demonstrate that EC significantly promotes GPP. Reflective processing (RP) and impulsive processing (IP) jointly mediate the relationship between EC and GPP through dual pathways. CL weakens both the direct effect of EC and the mediating effect of RP. Moreover, DO effectively reduces consumers’ CL, thereby facilitating green consumption behavior. These findings offer actionable insights for online platforms and green enterprises seeking to promote sustainable consumption through nudging-based design strategies.
Current studies on multilayer public goods games lack effective cross-layer incentive coordination mechanisms, particularly when individuals participate in multiple social layers with heterogeneous costs and misaligned incentives. To address this, we propose a reputation-based coupling mechanism on a two-layer scale-free network, examining how reputation feedback shapes cooperation under heterogeneous investment and reward conditions. In our model, agents accumulate payoffs independently per layer and update strategies via the Fermi rule, while their reputation-determined by payoff ratios across layers-governs context-dependent rewards and penalties. Simulation results show that higher investment costs suppress cooperation in the affected layer, whereas larger enhancement factors promote it, especially under homogeneous settings. Notably, adjusting reputation thresholds significantly improves the effectiveness of strict reward-punishment regimes over lenient ones. An asymmetric threshold design-combining a low reward threshold with a high punishment threshold-further enhances cooperation by concentrating incentives at the cooperative-defective boundary. This framework offers a theoretical basis for designing adaptive cooperation-promoting strategies.
To address the problems of difficult equilibrium achievement and stability caused by neglecting data owners’ bounded rationality and risk-averse behavior in data pricing, this paper establishes a discrete-time duopoly Bertrand game dynamic model that incorporates data accuracy, scale, and these behavioral factors. It derives a necessary and sufficient condition for the Nash equilibrium to be locally asymptotically stable. Theoretical analysis and numerical simulations show that increasing data accuracy or scale by a bounded rationality owner decreases Nash equilibrium stability, whereas such increases by an adaptive expectation owner enhance it. This means that data owners need to weigh the impact of data quality on pricing strategies. Moreover, when the data pricing system enters unstable periods, data owners’ average expected utility exhibits a declining trend. Therefore, we adopt an adaptive adjustment mechanism to suppress chaos, enabling pricing strategies to stabilize at equilibrium after a finite number of games.
In social network group decision-making (SNGDM), the interactions between the moderator and decision makers (DMs), as well as the mutual influences among DMs themselves, together with their fairness concerns within the social network, jointly determine the cost efficiency of consensus-reaching and the stability of the outcomes. However, existing consensus models are mostly constructed from a single perspective, failing to capture the interest game between the moderator and DMs simultaneously. Moreover, they often overlook the dynamic coupling relationship among opinion evolution, social relationships, and DMs’ coordination willingness, as well as its complex impact on fairness concerns. To address these challenges, this paper proposes a maximum-fairness and minimum-cost consensus model (MFMCCM) based on the Stackelberg game, aiming to achieve a synergistic optimization of fairness perception and consensus cost in dynamic social networks. Specifically, we construct a bi-level decision-making and optimization framework that incorporates a dynamic social relationship updating mechanism: the moderator, acting as the leader, guides consensus by designing compensation strategies; the DMs, as followers, dynamically adjust their modified opinions and coordination willingness based on compensation comparisons within the social network, aiming to maximize their fairness utility. Building upon this framework, we prove the existence of the game equilibrium and provide a detailed characterization of the DMs’ modification strategies under given compensation strategies. Furthermore, an optimal-response iterative algorithm is designed to determine the equilibrium strategies of DMs. Finally, numerical experiments and comparative analysis validate the effectiveness and superiority of the proposed model.
